Question which I have is that for clustering we use mahout but are there any specific scenarios in which mahout gives the better performance?I mean to say the specific problems types for which it gives a better result over others?
Gives better results than what? And "better" in the sense of faster, or "more accurate"?
The clustering algorithms in Mahout are fairly standard algorithms, not some special approach. So I think they perform as well as any other implementation of these standard algorithms in terms of quality.
In terms of performance -- they are implemented on Hadoop. This means it is much easier to scale up to very large data sets, but means you incur a lot of Hadoop overhead. For small data sets, you could probably find a faster implementation that is all on one machine, maybe something written in R. For very large data sets, where you can't apply non-distributed tools, I imagine it's about as good as anything else freely available out there. Honestly I'm not aware of another distributed clustering package to compare to.
Joined: Jun 11, 2008
Thanks for your reply. Yes I was asking in terms of the accuracy and performance both.
subject: Specific problem domains in which which mahout is best